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        <a href="cornetto-module.html">Package&nbsp;cornetto</a> ::
        <a href="cornetto.simcornet-module.html">Module&nbsp;simcornet</a> ::
        Class&nbsp;SimCornet
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class SimCornet</h1><p class="nomargin-top"><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet">source&nbsp;code</a></span></p>
<pre class="base-tree">
   object --+    
            |    
<a href="cornetto.cornet.Cornet-class.html">cornet.Cornet</a> --+
                |
               <strong class="uidshort">SimCornet</strong>
</pre>

<hr />
<p>An extension of the Cornet class which adds word similarity measures. 
  This assumes counts are added to the Cornetto database.</p>

<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
<table class="summary" border="1" cellpadding="3"
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         ><span class="options">[<a href="#section-InstanceMethods"
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#open" class="summary-sig-name">open</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">cdb_lu</span>,
        <span class="summary-sig-arg">cdb_syn</span>,
        <span class="summary-sig-arg">verbose</span>=<span class="summary-sig-default">False</span>)</span><br />
      Open and parse Cornetto database files with counts</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.open">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">dict</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#get_count" class="summary-sig-name">get_count</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Get (sub)counts for lexical units satisfying this specification</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_count">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">dict</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#get_total_counts" class="summary-sig-name">get_total_counts</a>(<span class="summary-sig-arg">self</span>)</span><br />
      Get the total counts per category and overall</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_total_counts">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">dict</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#get_probability" class="summary-sig-name">get_probability</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Get probability (p) for lexical units satisfying this specification, 
      where the probability is defined as lu_count / total_count.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_probability">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">dict</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#get_info_content" class="summary-sig-name">get_info_content</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Get information content (IC) for lexical units satisfying this 
      specification, defined as the negative log of the lexical unit's 
      probability, i.e.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_info_content">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">float or None</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#resnik_sim" class="summary-sig-name">resnik_sim</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec1</span>,
        <span class="summary-sig-arg">lu_spec2</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Compute the semantic similarity as decribed in Philip Resnik's paper 
      &quot;Using Information Content to Evaluate Semantic Similarity in a 
      Taxonomy&quot; (1995).</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.resnik_sim">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">float or None</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#jiang_conrath_dist" class="summary-sig-name">jiang_conrath_dist</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec1</span>,
        <span class="summary-sig-arg">lu_spec2</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Compute the semantic distance as decribed in Jay Jiang &amp; David 
      Conrath's paper &quot;Semantic Similarity Based on Corpus Statistics 
      and Lexical Taxonomy&quot; (1997).</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.jiang_conrath_dist">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">float or None</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#jiang_conrath_sim" class="summary-sig-name">jiang_conrath_sim</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec1</span>,
        <span class="summary-sig-arg">lu_spec2</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns Jiang &amp; Conrath's distance converted to a similarity by 
      means of sim = 1 / (1 + dist).</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.jiang_conrath_sim">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">float or None</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="cornetto.simcornet.SimCornet-class.html#lin_sim" class="summary-sig-name">lin_sim</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu_spec1</span>,
        <span class="summary-sig-arg">lu_spec2</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">None</span>)</span><br />
      Compute the semantic similarity as decribed in the paper Dekang Lin's
      paper &quot;An information-theoretic definition of similarity&quot; 
      (1998).</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.lin_sim">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr class="private">
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_get_lu_count"></a><span class="summary-sig-name">_get_lu_count</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>)</span><br />
      get (sub)count of lexical unit</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet._get_lu_count">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr class="private">
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_p"></a><span class="summary-sig-name">_p</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>)</span><br />
      probility on the basis of MLE using (sub)counts</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet._p">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr class="private">
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_IC"></a><span class="summary-sig-name">_IC</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">lu</span>,
        <span class="summary-sig-arg">subcount</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">smooth</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">cat_totals</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">base</span>=<span class="summary-sig-default">2</span>)</span><br />
      Information Content</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet._IC">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="cornetto.cornet.Cornet-class.html">cornet.Cornet</a></code></b>:
      <code><a href="cornetto.cornet.Cornet-class.html#__init__">__init__</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#all_common_subsumers">all_common_subsumers</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#ask">ask</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_lex_unit_by_id">get_lex_unit_by_id</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_lex_units">get_lex_units</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_related_lex_units">get_related_lex_units</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_related_synsets">get_related_synsets</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_synset_by_id">get_synset_by_id</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#get_synsets">get_synsets</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#least_common_subsumers">least_common_subsumers</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#set_max_depth">set_max_depth</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#set_output_format">set_output_format</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#test_lex_units_relation">test_lex_units_relation</a></code>
      </p>
    <div class="private">    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="cornetto.cornet.Cornet-class.html">cornet.Cornet</a></code></b> (private):
      <code><a href="cornetto.cornet.Cornet-class.html#_bidirectional_shortest_path" onclick="show_private();">_bidirectional_shortest_path</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_lex_unit_formatter" onclick="show_private();">_get_lex_unit_formatter</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_lu_cat" onclick="show_private();">_get_lu_cat</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_lu_form" onclick="show_private();">_get_lu_form</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_lu_sense" onclick="show_private();">_get_lu_sense</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_rel_name" onclick="show_private();">_get_rel_name</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_relation_formatter" onclick="show_private();">_get_relation_formatter</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_get_synset_formatter" onclick="show_private();">_get_synset_formatter</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_lu_has_cat" onclick="show_private();">_lu_has_cat</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_lu_has_sense" onclick="show_private();">_lu_has_sense</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_lu_to_spec" onclick="show_private();">_lu_to_spec</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_reconstruct_path" onclick="show_private();">_reconstruct_path</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_rel_has_name" onclick="show_private();">_rel_has_name</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_rel_to_spec" onclick="show_private();">_rel_to_spec</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_rel_to_xml" onclick="show_private();">_rel_to_xml</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_search_related_lex_units" onclick="show_private();">_search_related_lex_units</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_split_query" onclick="show_private();">_split_query</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_split_rel_spec" onclick="show_private();">_split_rel_spec</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_split_unit_spec" onclick="show_private();">_split_unit_spec</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_synset_to_specs" onclick="show_private();">_synset_to_specs</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_transitive_closure" onclick="show_private();">_transitive_closure</a></code>
      </p></div>
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__delattr__</code>,
      <code>__format__</code>,
      <code>__getattribute__</code>,
      <code>__hash__</code>,
      <code>__new__</code>,
      <code>__reduce__</code>,
      <code>__reduce_ex__</code>,
      <code>__repr__</code>,
      <code>__setattr__</code>,
      <code>__sizeof__</code>,
      <code>__str__</code>,
      <code>__subclasshook__</code>
      </p>
    </td>
  </tr>
</table>
<!-- ==================== CLASS VARIABLES ==================== -->
<a name="section-ClassVariables"></a>
<table class="summary" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Class Variables</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-ClassVariables"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
  <tr>
    <td colspan="2" class="summary">
    <div class="private">    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="cornetto.cornet.Cornet-class.html">cornet.Cornet</a></code></b> (private):
      <code><a href="cornetto.cornet.Cornet-class.html#_default_max_depth" onclick="show_private();">_default_max_depth</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_default_output_format" onclick="show_private();">_default_output_format</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_handled_output_formats" onclick="show_private();">_handled_output_formats</a></code>,
      <code><a href="cornetto.cornet.Cornet-class.html#_unit_separator" onclick="show_private();">_unit_separator</a></code>
      </p></div>
    </td>
  </tr>
</table>
<!-- ==================== PROPERTIES ==================== -->
<a name="section-Properties"></a>
<table class="summary" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Properties</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-Properties"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__class__</code>
      </p>
    </td>
  </tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
<a name="section-MethodDetails"></a>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Method Details</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-MethodDetails"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
</table>
<a name="open"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">open</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">cdb_lu</span>,
        <span class="sig-arg">cdb_syn</span>,
        <span class="sig-arg">verbose</span>=<span class="sig-default">False</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.open">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Open and parse Cornetto database files with counts</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>cdb_lu</code></strong> (file or filename) - xml definition of the lexical units with counts</li>
        <li><strong class="pname"><code>cdb_syn</code></strong> (file or filename) - xml definition of the synsets</li>
        <li><strong class="pname"><code>verbose</code></strong> - verbose output during parsing</li>
    </ul></dd>
    <dt>Overrides:
        <a href="cornetto.cornet.Cornet-class.html#open">cornet.Cornet.open</a>
    </dt>
  </dl>
</td></tr></table>
</div>
<a name="get_count"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">get_count</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec</span>,
        <span class="sig-arg">subcount</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_count">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Get (sub)counts for lexical units satisfying this specification</p>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>c.get_counts(<span class="py-string">&quot;varen&quot;</span>)
<span class="py-output">{'varen:noun:1': 434,</span>
<span class="py-output"> 'varen:verb:1': 15803,</span>
<span class="py-output"> 'varen:verb:2': 15803,</span>
<span class="py-output"> 'varen:verb:3': 15803}</span></pre>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>pprint(c.get_counts(<span class="py-string">&quot;varen&quot;</span>, subcount=True))
<span class="py-output">{'varen:noun:1': 434,</span>
<span class="py-output"> 'varen:verb:1': 18977,</span>
<span class="py-output"> 'varen:verb:2': 62086,</span>
<span class="py-output"> 'varen:verb:3': 15803}</span></pre>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec</code></strong> - lexical unit specification</li>
        <li><strong class="pname"><code>subcount</code></strong> (bool) - return subcounts instead of plain counts</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: dict</dt>
        <dd>mapping of lexical units in requested output format to 
          (sub)counts</dd>
  </dl>
<div class="fields">      <p><strong>Note:</strong>
        As the counts are currently based on lemma plus part-of-speech, not
        on the sense, they are the same for all senses of the same 
        category,
      </p>
</div></td></tr></table>
</div>
<a name="get_total_counts"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">get_total_counts</span>(<span class="sig-arg">self</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_total_counts">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Get the total counts per category and overall</p>
  <p>The categories are &quot;noun&quot;, &quot;verb&quot;, 
  &quot;adj&quot;, &quot;other&quot;; &quot;all&quot; represents the 
  overall count.</p>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>c.get_total_counts()
<span class="py-output">{'adj': 62156445,</span>
<span class="py-output"> 'all': 518291832,</span>
<span class="py-output"> 'noun': 187143322,</span>
<span class="py-output"> 'other': 199269966,</span>
<span class="py-output"> 'verb': 69722099}</span></pre>
  <dl class="fields">
    <dt>Returns: dict</dt>
        <dd>mapping of categories to counts</dd>
  </dl>
</td></tr></table>
</div>
<a name="get_probability"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">get_probability</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec</span>,
        <span class="sig-arg">subcount</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_probability">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Get probability (p) for lexical units satisfying this specification, 
  where the probability is defined as lu_count / total_count.</p>
  <p>By default, the total count is taken to be the sum of counts over all 
  word forms in the Cornetto database. However, when comparing two words of
  the same category (nouns, verbs, adjectives) it may be more appropriate 
  to take the sum over only the word forms of this category. This method is
  used if the keyword &quot;cat_totals&quot; is true.</p>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>c.get_probabilities(<span class="py-string">&quot;varen&quot;</span>)
<span class="py-output">{'varen:noun:1': 8.3736608066013281e-07,</span>
<span class="py-output"> 'varen:verb:1': 3.0490544176663777e-05,</span>
<span class="py-output"> 'varen:verb:2': 3.0490544176663777e-05,</span>
<span class="py-output"> 'varen:verb:3': 3.0490544176663777e-05}</span></pre>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec</code></strong> (string) - lexical unit specification</li>
        <li><strong class="pname"><code>subcount</code></strong> (bool) - use subcounts instead of plain counts</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth counts by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: dict</dt>
        <dd>mapping of lexical units in requested output format to 
          probabilties</dd>
  </dl>
</td></tr></table>
</div>
<a name="get_info_content"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">get_info_content</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec</span>,
        <span class="sig-arg">subcount</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.get_info_content">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Get information content (IC) for lexical units satisfying this 
  specification, defined as the negative log of the lexical unit's 
  probability, i.e. -log_2(lu_count / total_count)</p>
  <p>If a lexical unit has a count of zero, the probability is zero, the 
  log is undefined, and None is returned. Unless the keyword 
  &quot;smooth&quot; is true, in which case the count is smoothed by adding
  one.</p>
  <p>If no lexical unit matches the specification, an empty mapping is 
  returned.</p>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>pprint(c.get_info_content(<span class="py-string">&quot;plant&quot;</span>))
<span class="py-output">{'plant:noun:1': 14.51769181264614}</span></pre>
<pre class="py-doctest">
<span class="py-prompt">&gt;&gt;&gt; </span>pprint(c.get_info_content(<span class="py-string">&quot;plant&quot;</span>, subcount=True))
<span class="py-output">{'plant:noun:1': 10.482770362490861}</span></pre>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec</code></strong> (string) - lexical unit specification</li>
        <li><strong class="pname"><code>subcount</code></strong> (bool) - use subcounts instead of plain counts</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth counts by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: dict</dt>
        <dd>mapping of lexical units in requested output format to 
          information content</dd>
  </dl>
</td></tr></table>
</div>
<a name="resnik_sim"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">resnik_sim</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec1</span>,
        <span class="sig-arg">lu_spec2</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.resnik_sim">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Compute the semantic similarity as decribed in Philip Resnik's paper 
  &quot;Using Information Content to Evaluate Semantic Similarity in a 
  Taxonomy&quot; (1995). It is defined for a pair of concepts c1 and c2 
  as:</p>
  <p>argmax [IC(c1) + IC(c2) - 2 * IC(lcs)] for all lcs in LCS(c1, c2)</p>
  <p>In other words, the maximum value of the information content over all 
  least common subsumers of two concepts. An important point is that the 
  counts of an LCS, as used in computing its probabilty, is the sum of its 
  own count plus the counts of all concepts that it subsumes.</p>
  <p>As suggested by Resnik, it can be extended to _word_ similarity by 
  taking the maximum over the scores for all concepts that are senses of 
  the word. This means that if just two words are specified - without a 
  category or sense - two sets of matching lexical units are retrieved. For
  every combination of lexical units from these two sets, the LCS is 
  computed (if any), and the one with the maximum information content is 
  selected.</p>
  <p>If no LCS is found, this can mean two things:</p>
  <ol start="1">
    <li>
      The two words have no LCS because they truely have nothing in common.
      In this case we assume the LCS is zero and therefore we return zero.
    </li>
    <li>
      The two words should have something in common, but the correct LCS is
      not present in the Cornetto database. However, since there is no way 
      to know this, we consider this the same as (1), and zero is returned.
    </li>
  </ol>
  <p>There are two more marginal cases:</p>
  <ol start="1">
    <li>
      No lexical units in the Cornetto database match the specifications.
    </li>
    <li>
      All LCS have a subcount of zero, and no smoothing was applied, so its
      IC is undefined.
    </li>
  </ol>
  <p>In both cases None is returned.</p>
  <p>Notice that it is difficult to compare Resnik's word similarities, 
  because they depend on the subcounts. With identical words, for instance,
  resnik_sim(&quot;iets&quot;, &quot;iets&quot;) = 1.3113543459343666 
  whereas resnik_sim(&quot;spotje&quot;, &quot;spotje&quot;) = 
  25.141834494846584</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec1</code></strong> - first lexical unit(s) specification</li>
        <li><strong class="pname"><code>lu_spec2</code></strong> - second lexical unit(s) specification</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: float or None</dt>
        <dd>similarity score greater than or equal to zero</dd>
  </dl>
</td></tr></table>
</div>
<a name="jiang_conrath_dist"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">jiang_conrath_dist</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec1</span>,
        <span class="sig-arg">lu_spec2</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.jiang_conrath_dist">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Compute the semantic distance as decribed in Jay Jiang &amp; David 
  Conrath's paper &quot;Semantic Similarity Based on Corpus Statistics and 
  Lexical Taxonomy&quot; (1997). It is defined for a pair of concepts c1 
  and c2 as:</p>
  <p>argmin [IC(c1) + IC(c2) - 2 * IC(lcs)] for all lcs in LCS(c1, c2)</p>
  <p>This is without the edge and node weighting scheme, which is not 
  implemented here. The measure is extended to a _word_ distance measure by
  taking the minimum over the scores for all concepts (lexical units) that 
  are senses of the word (cf. doc of resnik_sim).</p>
  <p>If no LCS is found, this can mean two things:</p>
  <ol start="1">
    <li>
      The two words have no LCS because they truely have nothing in common.
      In this case we assume the LCS is zero and therefore we return the 
      minimun of IC(c1) + IC(c2).
    </li>
    <li>
      The two words should have something in common, but the correct LCS is
      not present in the Cornetto database. However, since there is no way 
      to know this, we consider this the same as (1), and we return the 
      minimun of IC(c1) + IC(c2).
    </li>
  </ol>
  <p>There are three more marginal cases:</p>
  <ol start="1">
    <li>
      No lexical units in the Cornetto database match the specifications.
    </li>
    <li>
      All matching lexical units have a subcount of zero, and no smoothing 
      was applied, so their IC is undefined.
    </li>
    <li>
      All LCS have a subcount of zero, and no smoothing was applied, so its
      IC is undefined. This implies that all subsumed lexical units must 
      have a subcount of zero, and therefore (2) must be the case as well.
    </li>
  </ol>
  <p>In all of these three cases None is returned.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec1</code></strong> - first lexical unit(s) specification</li>
        <li><strong class="pname"><code>lu_spec2</code></strong> - second lexical unit(s) specification</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: float or None</dt>
        <dd>distance greater than of equal to zero</dd>
  </dl>
</td></tr></table>
</div>
<a name="jiang_conrath_sim"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">jiang_conrath_sim</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec1</span>,
        <span class="sig-arg">lu_spec2</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.jiang_conrath_sim">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Returns Jiang &amp; Conrath's distance converted to a similarity by 
  means of sim = 1 / (1 + dist). See jiang_conrath_dist</p>
  <p>If the distance is None, so is the similarity.</p>
  <p>The translation from distance to similarity is not uniform. That is, 
  the space between the distances 1 and 2 and between the distances 2 and 3
  is the same (i.e. 1), but the space between the corresponding 
  similaraties, i.e. between 0.5 and 0.33 and between 0.33 and 0.25, is 
  not.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec1</code></strong> - first lexical unit(s) specification</li>
        <li><strong class="pname"><code>lu_spec2</code></strong> - second lexical unit(s) specification</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: float or None</dt>
        <dd>similarity score between zero and one included.</dd>
  </dl>
</td></tr></table>
</div>
<a name="lin_sim"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">lin_sim</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">lu_spec1</span>,
        <span class="sig-arg">lu_spec2</span>,
        <span class="sig-arg">smooth</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">cat_totals</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="cornetto.simcornet-pysrc.html#SimCornet.lin_sim">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Compute the semantic similarity as decribed in the paper Dekang Lin's 
  paper &quot;An information-theoretic definition of similarity&quot; 
  (1998). It is defined for a pair of concepts c1 and c2 as:</p>
  <p>argmax [2 * IC(lcs) / (IC(c1) + IC(c2))] for all lcs in LCS(c1, 
  c2)</p>
  <p>This measure is extended to a _word_ distance measure by taking the 
  maximum over the scores for all concepts (lexical units) that are senses 
  of the word (cf. doc of resnik_sim).</p>
  <p>If no LCS is found, this can mean two things:</p>
  <ol start="1">
    <li>
      The two words have no LCS because they truely have nothing in common.
      In this case we assume the IC of the LCS is zero and we return zero.
    </li>
    <li>
      The two words should have something in common, but the correct LCS is
      not present in the Cornetto database. However, since there is no way 
      to know this, we consider this the same as (1), and we return zero.
    </li>
  </ol>
  <p>There are three more marginal cases:</p>
  <ol start="1">
    <li>
      No lexical units in the Cornetto database match the specifications.
    </li>
    <li>
      All matching lexical units have a subcount of zero, and no smoothing 
      was applied, so their IC is undefined.
    </li>
    <li>
      All LCS have a subcount of zero, and no smoothing was applied, so its
      IC is undefined. This implies that all subsumed lexical units must 
      have a subcount of zero, and therefore (2) must be the case as well.
    </li>
  </ol>
  <p>In all of these three cases None is returned.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>lu_spec1</code></strong> - first lexical unit(s) specification</li>
        <li><strong class="pname"><code>lu_spec2</code></strong> - second lexical unit(s) specification</li>
        <li><strong class="pname"><code>smooth</code></strong> (bool) - smooth by adding one to lexical units with a zero count</li>
        <li><strong class="pname"><code>cat_totals</code></strong> (bool) - use total count for category of lexical unit instead of overall 
          total count</li>
        <li><strong class="pname"><code>format</code></strong> ('spec', 'xml', 'raw') - output format</li>
    </ul></dd>
    <dt>Returns: float or None</dt>
        <dd>similarity score between zero and one included</dd>
  </dl>
</td></tr></table>
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